Partial Motion Imitation for Learning Cart Pushing with Legged Manipulators

Partial Motion Imitation for Learning Cart Pushing with Legged Manipulators | Signal Canvas | ScienceToStartup

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Canonical route: /signal-canvas/partial-motion-imitation-for-learning-cart-pushing-with-legged-manipulators

stale

Proof freshness: stale
Proof status: unverified
Display score: 7/10
Last proof check: 2026-03-30
Score updated: 2026-04-02
Score fresh until: 2026-05-02
References: 51
Source count: 3
Coverage: 50%

This page is showing the last landed evidence receipt and score bundle because the latest proof data is outside the freshness window.

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Canonical ID partial-motion-imitation-for-learning-cart-pushing-with-legged-manipulators | Route /signal-canvas/partial-motion-imitation-for-learning-cart-pushing-with-legged-manipulators

REST example

curl https://sciencetostartup.com/api/v1/agent-handoff/signal-canvas/partial-motion-imitation-for-learning-cart-pushing-with-legged-manipulators

MCP example

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    "paper_ref": "partial-motion-imitation-for-learning-cart-pushing-with-legged-manipulators",
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}

source_context

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  "query": "Partial Motion Imitation for Learning Cart Pushing with Legged Manipulators",
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Evidence Receipt

Route status: building

Claims: 7

References: 51

Proof: Verification pending

Freshness state: computing

Source paper: Partial Motion Imitation for Learning Cart Pushing with Legged Manipulators

PDF: https://arxiv.org/pdf/2603.26659v1

Source count: 3

Coverage: 50%

Last proof check: 2026-03-30T21:51:33.786Z

Signal Canvas receipt window

Watch and verify: Partial Motion Imitation for Learning Cart Pushing with Legged Manipulators

/buildability/partial-motion-imitation-for-learning-cart-pushing-with-legged-manipulators

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Subject: Partial Motion Imitation for Learning Cart Pushing with Legged Manipulators

Verdict

Watch

Verdict is Watch because viability or proof quality is intermediate and should be re-evaluated before execution.

Preparing verified analysis

GitHub Code Pulse

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Claim map

Strong 7Mixed 0Weak 0

Evidencepartial
This work proposes a partial imitation learning approach that transfers the locomotion style learned from a locomotion task to cart loco-manipulation.
Implicationpartial
This is a core methodological claim stated in the abstract and elaborated upon in the introduction.
Verificationpartial
partial
Evidencepartial
We conduct experiments demonstrating that the learned policy successfully pushes a cart along diverse trajectories in IsaacLab and transfers effectively to MuJoCo.
Implicationpartial
This is a key result claim stated in the abstract and supported by the experimental results in Table V.
Verificationpartial
partial
Evidencepartial
We also compare our method to several baselines and show that the proposed approach achieves more stable and accurate loco-manipulation behaviors.
Implicationpartial
This is a comparative result claim stated in the abstract and supported by the quantitative data in Table V.
Verificationpartial
partial
Evidencepartial
Our platform is a legged manipulator consisting of a WidowX AI arm [39] mounted on a Unitree Go2 quadruped [40], tasked with pushing a kid-sized shopping cart [41].
Implicationpartial
This describes the specific technical setup and task, which is crucial for understanding the experiments.
Verificationpartial
partial
Evidencepartial
A robust locomotion policy is first trained with extensive domain and terrain randomization...
Implicationpartial
This details a specific aspect of the training methodology that contributes to robustness.
Verificationpartial
partial
Evidencepartial
Partial AMP (ours)99.9±0.10.045±0.000 0.085±0.001 0.035±0.001 0.038±0.0000.293±0.001
Implicationpartial
This is a specific, verifiable quantitative result comparing the proposed method to baselines.
Verificationpartial
partial
Evidencepartial
Full AMP tends to overfit to the arm states observed during training, often keeping the arm close to the base, resulting in unstable contact behavior
Implicationpartial
This is a specific limitation identified for a baseline method, explaining a failure mode.
Verificationpartial
partial

Author intelligence and commercialization panels stay hidden until the proof receipt is verified, cites at least 3 references, includes at least 2 sources, and clears 50% coverage. The paper narrative and citation surfaces remain public while verification is pending.

Partial Motion Imitation for Learning Cart Pushing with Legged Manipulators

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